<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Evaluation of seismic signals is one of the most important research topics on Volcanology. Volcanoes have daily activity; therefore, high speed evaluation of recorded signals is a challenge for improving the study of the natural phenomena occurring inside these natural formations. The aim of this paper is the evaluation (denoising, localization and classification) and analysis of Llaima volcano activities, one of the most actives volcanoes in South America. Different already proposed methods, such as, Butterworth, Spectral Subtraction (SS) and Wiener Filter (WF) are compared to the proposed Modified Spectral Subtraction (MSS) and Modified Wiener Filter (MWF) to find the best method for denoising the volcano signals. Then, event localization based on received signals of volcano is performed. In this step, Time Delay Estimation (TDE)-based method is used on data acquired from 3 mechanical sensors located in the volcano area. The proposed method is used to estimate the area for event location. The proposed denoising methods make the starting point for the event more evident to increase the localization accuracy for events where the starting point is difficult to find. In the last step, a method based on the novel DNN technique is proposed to classify the three main events occurring in the Llaima volcano (TR (Tremor), LP (Long Period) and VT (Volcano Tectonic)).</p></abstract>

Evaluation of seismic signals is one of the most important research topics on Volcanology. Volcanoes have daily activity; therefore, high speed evaluation of recorded signals is a challenge for improving the study of the natural phenomena occurring inside these natural formations. The aim of this paper is the evaluation (denoising, localization and classification) and analysis of Llaima volcano activities, one of the most actives volcanoes in South America. Different already proposed methods, such as, Butterworth, Spectral Subtraction (SS) and Wiener Filter (WF) are compared to the proposed Modified Spectral Subtraction (MSS) and Modified Wiener Filter (MWF) to find the best method for denoising the volcano signals. Then, event localization based on received signals of volcano is performed. In this step, Time Delay Estimation (TDE)-based method is used on data acquired from 3 mechanical sensors located in the volcano area. The proposed method is used to estimate the area for event location. The proposed denoising methods make the starting point for the event more evident to increase the localization accuracy for events where the starting point is difficult to find. In the last step, a method based on the novel DNN technique is proposed to classify the three main events occurring in the Llaima volcano (TR (Tremor), LP (Long Period) and VT (Volcano Tectonic)).

Evaluation of Llaima volcano activities for localization and classification of LP, VT and TR events

Observatorio Vulcanolgico de Los Andes del Sur (OVDAS)

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Evaluation of seismic signals is one of the most important research topics on Volcanology. Volcanoes have daily activity; therefore, high speed evaluation of...

Evaluation of Llaima volcano activities for localization and classification of LP, VT and TR events

Pubblicato online: 28 nov 2017

Pagine: 325 - 338

Ricevuto: 13 apr 2017

DOI: https://doi.org/10.1515/jee-2017-0064

Parole chiave
seismic signals, Llaima volcano, spectral subtraction, wiener filter, deep neural network, source localization

© 2017 Ali Dehghan Firoozabadi et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Evaluation of Llaima volcano activities for localization and classification of LP, VT and TR events

Pubblicato online: 28 nov 2017

Pagine: 325 - 338

Ricevuto: 13 apr 2017

DOI: https://doi.org/10.1515/jee-2017-0064

Parole chiaveseismic signals, Llaima volcano, spectral subtraction, wiener filter, deep neural network, source localization

© 2017 Ali Dehghan Firoozabadi et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
seismic signals, Llaima volcano, spectral subtraction, wiener filter, deep neural network, source localization